Radiologists have to read many images of scans produced by computed tomography (CT), X-rays, magnetic resonance imaging (MRI), ultrasound, positron emission tomography (PET), etc. This may lead to “information overload” for radiologists. On the other side, radiologists may misinterpret scans thus leading to delays in treatment or unnecessary biopsies. Information overload potentially aggravates this problem. In such situations, decision support systems such as computer-aided diagnosis schemes are, as a consequence, increasingly being utilized to improve both workflow and patient outcome.
The background of computer-aided diagnosis systems is that clinicians acquire knowledge by experience in referring to cases that they have seen before. One way in which a decision support system can assist a clinician in making a diagnosis of, for example a CT scan of lung cancer, is to offer previous images that have been diagnosed and are similar to the new one. The scan can be generated by the same or any other modalities, such as X-rays, magnetic resonance imaging (MRI), ultrasound, positron emission tomography (PET), etc. An example-based (i.e. case-based) paradigm is that nodules with known diagnosis are retrieved from a database of prior cases and presented to the radiologist. This is the basic premise of an example-based CADx system.
WO patent application, entitled as “Clinician-driven example-based computer-aided diagnosis”, filed by Koninklijke Philips Electronics N.V with application number as IB2007/052307 and not published yet, describes a method and device for optimizing clinician-driven example-based computer-aided diagnosis system. According to the WO patent application, optimizing example-based (i.e. case-based) diagnosis is accomplished by clustering volumes-of-interest (VOIs) in a database into respective clusters according to subjective assessment of similarity. An optimal set of volume-of-interest features is then selected for fetching examples such that objective assessment of similarity, based on the selected features, clusters the database VOIs in a feature space so as to conform to the subjective-based clustering. The fetched examples are displayed alongside the VOI to be diagnosed for comparison by the clinician.